Python Generators

Generators helps to simplify the iterator concepts in python. The implementation of iter and next method is not required to build the traversal object in generator. With the help of yield keyword instead of return keyword. we can build iterable objects

Generators

The same ten multiples concepts can be implement in generators with few lines of code like the below example.

#Python Generators
def TenMultipler(multiple, max_limit):
    start = multiple * 10
    for num in range(start, max_limit+1, start):
        yield num

for each in TenMultipler(2, 100):
    print(each)

Output

20
40
60
80
100

Generator Expression

Generator Expressions are awesome feature like list comprehension, we can build generator for the entire list without user-defined function. The generator expressions are more memory efficient since it will generate item only when requested but list comprehension generates the entire items during conversion itself

input_list = [1, 2, 3, 4, 5]

#Generator Expression
result = ( each*10 for each in input_list )
print("Output result from generator expression: ")
#Generator item needs to generate one by one
for each in result: print(each)

#list comprehension
result = [ each*10 for each in input_list ]
print("Output result from list comprehension: ")
#No need to generate one by one like generator expression
print(result)

output

Output result from generator expression: 
10
20
30
40
50
Output result from list comprehension: 
[10, 20, 30, 40, 50]